IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v121y2018icp355-367.html
   My bibliography  Save this article

Dynamic performance of an aiming control methodology for solar central receivers due to cloud disturbances

Author

Listed:
  • García, Jesús
  • Soo Too, Yen Chean
  • Padilla, Ricardo Vasquez
  • Beath, Andrew
  • Kim, Jin-Soo
  • Sanjuan, Marco E.

Abstract

An appropriate control of the heat flux distribution over the solar central receiver is essential to achieve an efficient and safe operation of solar tower systems. High solar radiation variation due to moving clouds may cause failures to the solar receiver. This paper shows a dynamic performance analysis of a solar central receiver operating when short-time cloud passages partially shade the solar field. The solar receiver incorporates an aiming methodology based on a closed loop model predictive control approach. The DNI changes are simulated using an agent-based model that closely emulates the transients in solar radiation caused by clouds. These models are coupled with a solar system model that resembles the Gemasolar solar plant. The simulations showed that the base feedback loop aiming strategy could successfully restore the solar receiver back to its steady state after transient operations caused by clouds. However, undesired overshoots in incident flux density and high heating rates in the controlled variables were found. These issues are overcome through a setpoint readjustment approach, which is temporally supported by a PI controller. The results show that the proposed aiming control strategy can provide a continuous safe operation of the solar central receiver when subject to transient flux distribution due to clouds.

Suggested Citation

  • García, Jesús & Soo Too, Yen Chean & Padilla, Ricardo Vasquez & Beath, Andrew & Kim, Jin-Soo & Sanjuan, Marco E., 2018. "Dynamic performance of an aiming control methodology for solar central receivers due to cloud disturbances," Renewable Energy, Elsevier, vol. 121(C), pages 355-367.
    • Handle: RePEc:eee:renene:v:121:y:2018:i:c:p:355-367
    DOI: 10.1016/j.renene.2018.01.019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118300193
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2018.01.019?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Qing & Bai, Fengwu & Yang, Bei & Wang, Zhifeng & El Hefni, Baligh & Liu, Sijie & Kubo, Syuichi & Kiriki, Hiroaki & Han, Mingxu, 2016. "Dynamic simulation and experimental validation of an open air receiver and a thermal energy storage system for solar thermal power plant," Applied Energy, Elsevier, vol. 178(C), pages 281-293.
    2. Prasad, Abhnil A. & Taylor, Robert A. & Kay, Merlinde, 2015. "Assessment of direct normal irradiance and cloud connections using satellite data over Australia," Applied Energy, Elsevier, vol. 143(C), pages 301-311.
    3. Yu, Qiang & Wang, Zhifeng & Xu, Ershu, 2014. "Analysis and improvement of solar flux distribution inside a cavity receiver based on multi-focal points of heliostat field," Applied Energy, Elsevier, vol. 136(C), pages 417-430.
    4. García, Jesús M. & Padilla, Ricardo Vasquez & Sanjuan, Marco E., 2016. "A biomimetic approach for modeling cloud shading with dynamic behavior," Renewable Energy, Elsevier, vol. 96(PA), pages 157-166.
    5. Zhu, Jianqin & Wang, Kai & Wu, Hongwei & Wang, Dunjin & Du, Juan & Olabi, A.G., 2015. "Experimental investigation on the energy and exergy performance of a coiled tube solar receiver," Applied Energy, Elsevier, vol. 156(C), pages 519-527.
    6. Hassan, Gasser E. & Youssef, M. Elsayed & Mohamed, Zahraa E. & Ali, Mohamed A. & Hanafy, Ahmed A., 2016. "New Temperature-based Models for Predicting Global Solar Radiation," Applied Energy, Elsevier, vol. 179(C), pages 437-450.
    7. Mostafavi Tehrani, S. Saeed & Taylor, Robert A., 2016. "Off-design simulation and performance of molten salt cavity receivers in solar tower plants under realistic operational modes and control strategies," Applied Energy, Elsevier, vol. 179(C), pages 698-715.
    8. Le Brun, N. & Hewitt, G.F. & Markides, C.N., 2017. "Transient freezing of molten salts in pipe-flow systems: Application to the direct reactor auxiliary cooling system (DRACS)," Applied Energy, Elsevier, vol. 186(P1), pages 56-67.
    9. Sánchez-González, Alberto & Santana, Domingo, 2015. "Solar flux distribution on central receivers: A projection method from analytic function," Renewable Energy, Elsevier, vol. 74(C), pages 576-587.
    10. Collado, Francisco J. & Guallar, Jesús, 2013. "A review of optimized design layouts for solar power tower plants with campo code," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 142-154.
    11. Tsoutsos, Theocharis & Gekas, Vasilis & Marketaki, Katerina, 2003. "Technical and economical evaluation of solar thermal power generation," Renewable Energy, Elsevier, vol. 28(6), pages 873-886.
    12. Collado, Francisco J. & Guallar, Jesús, 2012. "Campo: Generation of regular heliostat fields," Renewable Energy, Elsevier, vol. 46(C), pages 49-59.
    13. Besarati, Saeb M. & Yogi Goswami, D., 2014. "A computationally efficient method for the design of the heliostat field for solar power tower plant," Renewable Energy, Elsevier, vol. 69(C), pages 226-232.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ellingwood, Kevin & Mohammadi, Kasra & Powell, Kody, 2020. "Dynamic optimization and economic evaluation of flexible heat integration in a hybrid concentrated solar power plant," Applied Energy, Elsevier, vol. 276(C).
    2. Alonso-Montesinos, J. & Monterreal, R. & Fernández-Reche, J. & Ballestrín, J. & Carra, E. & Polo, J. & Barbero, J. & Batlles, F.J. & López, G. & Enrique, R. & Martínez-Durbán, M. & Marzo, A., 2019. "Intra-hour energy potential forecasting in a central solar power plant receiver combining Meteosat images and atmospheric extinction," Energy, Elsevier, vol. 188(C).
    3. García, Jesús & Barraza, Rodrigo & Soo Too, Yen Chean & Vásquez-Padilla, Ricardo & Acosta, David & Estay, Danilo & Valdivia, Patricio, 2022. "Transient simulation of a control strategy for solar receivers based on mass flow valves adjustments and heliostats aiming," Renewable Energy, Elsevier, vol. 185(C), pages 1221-1244.
    4. García, Jesús & Barraza, Rodrigo & Soo Too, Yen Chean & Vásquez Padilla, Ricardo & Acosta, David & Estay, Danilo & Valdivia, Patricio, 2020. "Aiming clusters of heliostats over solar receivers for distributing heat flux using one variable per group," Renewable Energy, Elsevier, vol. 160(C), pages 584-596.
    5. Wang, Shuang & Asselineau, Charles-Alexis & Fontalvo, Armando & Wang, Ye & Logie, William & Pye, John & Coventry, Joe, 2023. "Co-optimisation of the heliostat field and receiver for concentrated solar power plants," Applied Energy, Elsevier, vol. 348(C).
    6. Ruidi Zhu & Dong Ni, 2023. "A Model Predictive Control Approach for Heliostat Field Power Regulatory Aiming Strategy under Varying Cloud Shadowing Conditions," Energies, MDPI, vol. 16(7), pages 1-19, March.
    7. Hu, Yeguang & Xu, Zhigang & Zhou, Chaoying & Du, Jianjun & Yao, Yingxue, 2020. "Design and performance analysis of a multi-reflection heliostat field in solar power tower system," Renewable Energy, Elsevier, vol. 160(C), pages 498-512.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Collado, Francisco J. & Guallar, Jesus, 2019. "Quick design of regular heliostat fields for commercial solar tower power plants," Energy, Elsevier, vol. 178(C), pages 115-125.
    2. Wang, Jianxing & Duan, Liqiang & Yang, Yongping, 2018. "An improvement crossover operation method in genetic algorithm and spatial optimization of heliostat field," Energy, Elsevier, vol. 155(C), pages 15-28.
    3. Saghafifar, Mohammad & Gadalla, Mohamed, 2017. "Thermo-economic optimization of hybrid solar Maisotsenko bottoming cycles using heliostat field collector: Comparative analysis," Applied Energy, Elsevier, vol. 190(C), pages 686-702.
    4. Saghafifar, Mohammad & Gadalla, Mohamed, 2017. "Thermo-economic evaluation of water-injected air bottoming cycles hybridization using heliostat field collector: Comparative analyses," Energy, Elsevier, vol. 119(C), pages 1230-1246.
    5. Zhang, Maolong & Xu, Chao & Du, Xiaoze & Amjad, Muhammad & Wen, Dongsheng, 2017. "Off-design performance of concentrated solar heat and coal double-source boiler power generation with thermocline energy storage," Applied Energy, Elsevier, vol. 189(C), pages 697-710.
    6. Abutayeh, Mohammad & Padilla, Ricardo Vasquez & Lake, Maree & Lim, Yee Yan & Garcia, Jesus & Sedighi, Mohammadreza & Soo Too, Yen Chean & Jeong, Kwangkook, 2019. "Effect of short cloud shading on the performance of parabolic trough solar power plants: motorized vs manual valves," Renewable Energy, Elsevier, vol. 142(C), pages 330-344.
    7. Ashley, Thomas & Carrizosa, Emilio & Fernández-Cara, Enrique, 2017. "Optimisation of aiming strategies in Solar Power Tower plants," Energy, Elsevier, vol. 137(C), pages 285-291.
    8. Piroozmand, Pasha & Boroushaki, Mehrdad, 2016. "A computational method for optimal design of the multi-tower heliostat field considering heliostats interactions," Energy, Elsevier, vol. 106(C), pages 240-252.
    9. Zaharaddeen Ali Hussaini & Peter King & Chris Sansom, 2020. "Numerical Simulation and Design of Multi-Tower Concentrated Solar Power Fields," Sustainability, MDPI, vol. 12(6), pages 1-22, March.
    10. Ortega, Guillermo & Rovira, Antonio, 2020. "A fast and accurate methodology for the calculation of the shading and blocking efficiency in central receiver systems," Renewable Energy, Elsevier, vol. 154(C), pages 58-70.
    11. Saghafifar, Mohammad & Gadalla, Mohamed & Mohammadi, Kasra, 2019. "Thermo-economic analysis and optimization of heliostat fields using AINEH code: Analysis of implementation of non-equal heliostats (AINEH)," Renewable Energy, Elsevier, vol. 135(C), pages 920-935.
    12. Wang, Shuang & Asselineau, Charles-Alexis & Fontalvo, Armando & Wang, Ye & Logie, William & Pye, John & Coventry, Joe, 2023. "Co-optimisation of the heliostat field and receiver for concentrated solar power plants," Applied Energy, Elsevier, vol. 348(C).
    13. Ortega, Guillermo & Rovira, Antonio, 2020. "A new method for the selection of candidates for shading and blocking in central receiver systems," Renewable Energy, Elsevier, vol. 152(C), pages 961-973.
    14. Conroy, Tim & Collins, Maurice N. & Fisher, James & Grimes, Ronan, 2018. "Thermal and mechanical analysis of a sodium-cooled solar receiver operating under a novel heliostat aiming point strategy," Applied Energy, Elsevier, vol. 230(C), pages 590-614.
    15. Saghafifar, Mohammad & Gadalla, Mohamed, 2016. "Thermo-economic analysis of air bottoming cycle hybridization using heliostat field collector: A comparative analysis," Energy, Elsevier, vol. 112(C), pages 698-714.
    16. Chao Li & Rongrong Zhai & Yongping Yang, 2017. "Optimization of a Heliostat Field Layout on Annual Basis Using a Hybrid Algorithm Combining Particle Swarm Optimization Algorithm and Genetic Algorithm," Energies, MDPI, vol. 10(11), pages 1-15, November.
    17. Serrano-Arrabal, J. & Serrano-Aguilera, J.J. & Sánchez-González, A., 2021. "Dual-tower CSP plants: optical assessment and optimization with a novel cone-tracing model," Renewable Energy, Elsevier, vol. 178(C), pages 429-442.
    18. Nicolás C. Cruz & José D. Álvarez & Juana L. Redondo & Jesús Fernández-Reche & Manuel Berenguel & Rafael Monterreal & Pilar M. Ortigosa, 2017. "A New Methodology for Building-Up a Robust Model for Heliostat Field Flux Characterization," Energies, MDPI, vol. 10(5), pages 1-17, May.
    19. Cruz, N.C. & Redondo, J.L. & Berenguel, M. & Álvarez, J.D. & Ortigosa, P.M., 2017. "Review of software for optical analyzing and optimizing heliostat fields," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1001-1018.
    20. Cruz, N.C. & Salhi, S. & Redondo, J.L. & Álvarez, J.D. & Berenguel, M. & Ortigosa, P.M., 2018. "Hector, a new methodology for continuous and pattern-free heliostat field optimization," Applied Energy, Elsevier, vol. 225(C), pages 1123-1131.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:121:y:2018:i:c:p:355-367. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.